In digital image processing, and particularly in digital radiography, various image processing methods have been applied to the digital image to increase the diagnostic usefulness of the image. For example in the field of chest radiography, the goal of these image processing methods is to reproduce faithfully or to enhance the detail in both the lungs and the mediastinum in spite of their often large differences in subject contrast. However, when these image processing methods are applied to the entire digital radiographic image, the resulting enhancement in the lung field may be destructively high, potentially decreasing the detectability of small lesions. To overcome this problem, anatomical structure-selective processing of chest radiographs is desirable to prevent the detrimental effects to one structure from outweighing the improvement to another structure.
McAdams et al (see "Histogram Directed Processing of Digital Chest Images" by H.P. McAdams et al, Investigative Radiology, March 1986, Vol. 21, pp. 253-259) have discussed anatomical-structure selective image processing as applied to digital chest radiography. They used the lung field and the mediastinum histograms individually to determine a lung/mediastinum gray level threshold. The individual histograms for the lung field and the mediastinum were constructed by a trackball-driven cursor outlining technique. The gray level threshold was selected from the gray levels at which the two histograms overlap. McAdams et al. presented impressive results of anatomical structure-selective image processing guided by a lung/mediastinum gray level threshold. However, their method for determining the gray level threshold required human intervention, and therefore it was impractical for routine application. Rosenfeld and De La Torre (see "Histogram Concavity Analysis as an Aid in Threshold Selection", IEEE Transactions on Systems Man and Cybernetics, Vol. SMC-13, 1983) proposed an algorithm that used the image histogram concavity to automatically determine a gray level threshold for the images containing at most two major gray level subpopulations. Although capable of being automated, their method is very noise sensitive. Furthermore, the threshold determined by this method always lies closer to the tallest peak in the histogram which does not prove to be satisfactory for chest radiography in general.
Another problem encountered in the effort to automate the process of image segmentation is the difficulty in determining whether and where a peak in the histogram is actually located. This problem is aggravated by the presence of noise in the image, which causes the peaks to appear as clusters of spikes.
It is therefore the object of the present invention to provide an improved digital processing method for automatically detecting peaks in the histogram of a digital image and a method of selecting gray level thresholds for segmenting a digital image into distinquishable structures, that is free from the shortcomings noted above.